EEG Brain Profile Decoding With Machine Learning for VR/AR Input
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Solution Overview
Problem
Existing VR/AR devices lack efficient and intuitive methods for user input, particularly in constrained environments like head-mounted computers, and face challenges in accurately interpreting neural activity due to the vast variability of brain responses to stimuli.
Innovation Solution
A method using EEG data combined with machine learning models, including neural activity encoders and multi-modal decoders, to interpret user intentions by integrating visual, audio, and language stimuli, constrained by known environments, enabling intuitive user input and authentication through brain biometrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional input methods (keyboard, touchscreen, controllers) are used in VR/AR devices, then user input capability is provided, but device complexity and form factor constraints are worsened
Solution Approach 1:
The patent replaces mechanical input devices (keyboards, touchscreens, physical controllers) with a neural-based input system that directly reads brain activity through EEG sensors. This substitution eliminates the need for complex mechanical components while enabling intuitive user input through neural signals, directly resolving the contradiction between ease of operation and device complexity.
Solution Approach 2:
The patent introduces an intermediary system consisting of EEG sensors, machine learning models, and neural activity encoders that translate brain activity into actionable input commands. This intermediary layer bridges the gap between neural activity and device control, enabling user input without requiring traditional mechanical interfaces, thus reducing device complexity while maintaining ease of operation.
2Measurement precision
If EEG data is collected and processed to determine brain profiles, then user authentication and input accuracy are improved, but computation complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models and neural activity encoders with large datasets of EEG data from multiple subjects. This pre-processing and pre-training work is done beforehand, allowing the system to make accurate authentication decisions with simpler real-time computation, thus improving measurement precision while managing computation complexity during actual use.
Solution Approach 2:
The patent segments the complex computation task into multiple components: EEG data collection, neural activity encoding, feature extraction, and classification. By dividing the computation into these manageable segments and processing them through specialized machine learning models, the system achieves high authentication accuracy while distributing computational complexity across different processing stages.
3Measurement precision
If multiple machine learning models are used to decode neural activity, then interpretation accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple machine learning models offline with extensive EEG data. This allows the models to learn complex neural patterns in advance, so that during real-time operation, the pre-trained models can quickly and accurately interpret neural activity without requiring extensive processing time, thus improving interpretation accuracy while minimizing processing time loss.
Solution Approach 2:
The patent implements dynamics by using adaptive machine learning models that can adjust their processing based on the specific neural activity patterns detected. The system dynamically selects and applies appropriate decoding strategies based on the current neural state, improving interpretation accuracy while optimizing processing time by avoiding unnecessary computational steps.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances user interaction accuracy and privacy by allowing direct neural input and authentication, reducing the need for additional hardware and computation complexity in small form factors.
Implementation Method 1
Neurons in the underlying brain tissue generate electrical activity in the form of ionic currents that can be measured as voltage differences in the electrodes
Data Source
AI summary
In an embodiment, a computer-implemented method for decoding neural activity is provided. In the method, at least one machine learning model is trained using a training data set of EEG data and concurrently collected environmental data collected from data collection participants. Once the at least one machine learning model is trained, EEG data measured from sensors attached to or near a user's head is received. Environmental data describing stimulus the user is exposed to concurrently with the measurement of the EEG data is also received. The EEG data and the environmental data is input into the at least one machine learning model to determine an inference related to the neural activity. Based on the inference, an operation of a computer program is altered.